Unit-Aware Differentially Private Quantum Machine Learning for Clinical and Wearable Health Data
Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these records may leak sensitive information when released.
ProofPaper ↗
Key points
- Differential privacy (DP) bounds this leakage by injecting noise whose total magnitude grows with the number of trainable parameters, a cost especially severe for small health cohorts.
- This motivates parameter efficient models including quantum machine learning (QML), whose variational circuits can represent complex decision boundaries with few trainable parameters.
- It raises a question: at matched input dimensionality and privacy budget, does a quantum model retain more utility than a classical one?
- We compare capacity-matched quantum, multilayer perceptron, and logistic regression models across five privacy budgets and five seeds.
Sources (1)
- [1]Unit-Aware Differentially Private Quantum Machine Learning for Clinical and Wearable Health DataarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:38 PM
Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these records may leak sensitive information when released.
Differential privacy (DP) bounds this leakage by injecting noise whose total magnitude grows with the number of trainable parameters, a cost especially severe for small health cohorts.
Extractive summary: sentences quoted from the sources.
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